Novel Classification Scheme for Multi Agents

Shaik, Mohammed Ali; Praveen, P.; Prakash, R. Vijaya

doi:10.51983/ajcst-2019.8.s3.2087

Cited by 18 publications

(1 citation statement)

References 12 publications

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“…A swarm system can be represented by a graph , where denotes a collection of nodes representing individuals, is a collection of edges that each describe the connection relationship between any two nodes, and stands for the adjacency matrix describing the association topology (coupling relationships and strength) of the swarm system 40 , 41 . When studying swarm dynamics using a graph, the Laplacian matrix and its algebraic eigenvalue properties are effective tools for describing and analysing graph topological structure 42 , and other concepts such as the connected graph 43 and the spanning tree 44 are also widely used in swarm dynamics.…”

Section: Related Workmentioning

confidence: 99%

A game-based approach for designing a collaborative evolution mechanism for unmanned swarms on community networks

Pan

et al. 2022

Sci Rep

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Intelligent and coordinated unmanned aerial vehicle (UAV) swarm combat will be the main mode of warfare in the future, and mechanistic design of autonomous cooperation within swarms is the key to enhancing combat effectiveness. Exploration of the essential features and patterns of autonomous collaboration in unmanned swarms has become the focus of scientific research and technological applications, in keeping with the evolving conceptions of the military theatre. However, given the unique attributes of the military and the novelty of the warfare mode of unmanned swarms, few achievements have been reported in the existing research. In this study, we analysed the military requirements of unmanned swarm operations and proposed an analytic framework for autonomous collaboration. Then, a literature review addressing swarm evolution dynamics, game-based swarm collaboration, and collaborative evolution on complex networks was conducted. Next, on the basis of the above work, we designed a community network for unmanned swarm cooperation and constructed a collaborative evolution model based on the multiplayer public goods game (PGG). Furthermore, according to the “network” and “model”, the dynamic evolution process of swarm collaboration was formally deduced. Finally, a simulation was conducted to analyse the influence of relevant parameters (i.e., swarm size, degree distribution, cost, multiplication factor) on the collaborative behaviour of unmanned swarms. According to the simulation results, some reasonable suggestions for collaborative management and control in swarm operation are given, which can provide theoretical reference and decision-making support for the design of coordination mechanisms and improved combat effectiveness in unmanned swarm operation.

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